Boosting Earth System Model Outputs And Saving PetaBytes in Their Storage Using Exascale Climate Emulators

We present the design and scalable implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in...

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Bibliographic Details
Published in:SC24: International Conference for High Performance Computing, Networking, Storage and Analysis pp. 1 - 12
Main Authors: Abdulah, Sameh, Baker, Allison H., Bosilca, George, Cao, Qinglei, Castruccio, Stefano, Genton, Marc G., Keyes, David E., Khalid, Zubair, Ltaief, Hatem, Song, Yan, Stenchikov, Georgiy L., Sun, Ying
Format: Conference Proceeding
Language:English
Published: IEEE 17.11.2024
Subjects:
Analytical models
Computational modeling
Data models
Dynamic runtime systems
Earth
Emulation
Harmonic analysis
High-performance computing
Meteorology
Mixed-precision computation
Runtime
Spatial resolution
Spatio-temporal climate emulation
Spherical harmonic transform
Task-based programming models
Transforms
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Summary:We present the design and scalable implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in climate data. This provides tunable spatio-temporal resolution and significantly improves the fidelity and granularity of climate emulation, achieving an ultra-high spatial resolution of 0.034^{\circ}(\sim 3.5 \mathbf{~ k m}) in space. Our emulator, trained on 318 billion hourly temperature data points from a 35 -year and 31 billion daily data points from an 83-year global simulation ensemble, generates statistically consistent climate emulations. We extend linear solver software to mixed-precision arithmetic GPUs, applying different precisions within a single solver to adapt to different correlation strengths. The PaRSEC runtime system supports efficient parallel matrix operations by optimizing the dynamic balance between computation, communication, and memory requirements. Our BLAS3-rich code is optimized for systems equipped with four different families and generations of GPUs, scaling well to achieve 0.976 EFlop/s on 9, 025 nodes (36,100 AMD MI250X multichip module (MCM) GPUs) of Frontier (nearly full system), 0.739 EFlop/s on 1,936 nodes (7,744 Grace-Hopper Superchips (GH200)) of Alps, 0.243 EFlop/s on 1,024 nodes (4,096 A100 GPUs) of Leonardo, and 0.375 EFlop/s on 3,072 nodes (18,432 V100 GPUs) of Summit.
DOI:10.1109/SC41406.2024.00008